code: def softclip_constraint(low, high):
      """Maps `s` to support based on `low` and `high`."""
  
      def constrain(x):
          low_ = tf.convert_to_tensor(low)
          high_ = tf.convert_to_tensor(high)
          # Ensure the values are within (low, high).
          return (
              0.5 * (high_ - low_) * tf.math.sigmoid(x)
              + (high_ - low_) / 4
              + low_
          )
  
      return constrain
  
imports: import collections
  import inspect
  from absl import logging
  import hypothesis.strategies
  import tensorflow.compat.v2
  from tensorflow_probability.python import bijectors
  from tensorflow_probability.python.distributions import lkj
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import tensorshape_util
label_desc: Use image node_id 14 for calling a global function with example usage: softclip_constraint(low, high) and returns: constrain, unknown